ohi logo
OHI Science | Citation policy

[REFERENCE RMD FILE: https://cdn.rawgit.com/OHI-Science/ohiprep/master/globalprep/np/v2017/targetharvest_dataprep.html]

1 Summary

This analysis converts FAO capture production data into the OHI 2018 targeted harvest pressure data.

2 Updates from previous assessment

One more year of data

The species2group.csv file was udated: South America Sea Lion is not a cetacean therefore was assigned the pinniped order and excluded from the target list.


3 Data Source

http://www.fao.org/fishery/statistics/software/fishstatj/en#downlApp
 Release date: March 2018 

FAO Global Capture Production Quantity 1950_2016

Downloaded: Aug 1 2018

Description: Quantity (tonnes) of fisheries capture for each county, species, year.

Time range: 1950-2016


# load libraries, set directories
library(ohicore)  #devtools::install_github('ohi-science/ohicore@dev')
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(stringr)
library(tidyr)
library(ggplot2)
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
## comment out when knitting
#setwd("globalprep/prs_targetedharvest/v2018")


### Load FAO-specific user-defined functions
source('../../../src/R/fao_fxn.R') # function for cleaning FAO files
source('../../../src/R/common.R') # directory locations

4 Read in the raw data

This includes the FAO capture production data and a list of the “target” species.

## FAO capture production data
fis_fao_csv <-  read.csv(file.path(dir_M, 'git-annex/globalprep/_raw_data/FAO_capture/d2018/Global_capture_production_Quantity_1950-2016.csv'))


# species list 
sp2grp <-  read.csv('raw/species2group.csv') %>%
  dplyr::filter(incl_excl == 'include') %>%
  dplyr::select(target, species); head(sp2grp)
##     target                      species
## 1 cetacean     Atlantic spotted dolphin
## 2 cetacean Atlantic white-sided dolphin
## 3 cetacean   Australian snubfin dolphin
## 4 cetacean         Baird's beaked whale
## 5 cetacean            Baleen whales nei
## 6 cetacean            Beaked whales nei

5 Clean the FAO data

m <- fis_fao_csv %>%
  dplyr::rename(country = Country..Country.,
         species = Species..ASFIS.species.,
         area = Fishing.area..FAO.major.fishing.area.,
         Unit = Unit..Unit.) %>%
  dplyr::select(-Unit)

m <- m %>%
  tidyr::gather("year", "value", -(1:3)) %>%
  dplyr::mutate(year = gsub("X", "", year)) %>%
    fao_clean_data() 
## Warning: attributes are not identical across measure variables;
## they will be dropped
m <- m %>%
  dplyr::mutate(species = as.character(species)) %>%
  dplyr::mutate(species = ifelse(stringr::str_detect(species, "Henslow.*s swimming crab"), "Henslow's swimming crab", species))

6 Identify the target species

This analysis only includes target species. The warning messages need to be checked and, if necessary, changes should be made to the raw/species2group.csv

# check for discrepancies in species list
## seals are no longer included (so these errors can be ignored):
spgroups <-  sort(as.character(unique(m$species)))
groups <-  c('turtle', 'seal', 'whale', 'sea lion', 'dolphin', 'porpoise')

for (group in groups) { #group='dolphin'
possibles <- spgroups[grep(group, spgroups)]
d_missing_l <-  setdiff(possibles, sp2grp$species)
  if (length(d_missing_l)>0){
    cat(sprintf("\nMISSING in the lookup the following species in target='%s'.\n    %s\n", 
                group, paste(d_missing_l, collapse='\n    ')))
  }
}
## 
## MISSING in the lookup the following species in target='turtle'.
##     Chinese softshell turtle
##     River and lake turtles nei
## 
## MISSING in the lookup the following species in target='seal'.
##     Baikal seal
##     Bearded seal
##     Caspian seal
##     Grey seal
##     Harbour seal
##     Harp seal
##     Hooded seal
##     Larga seal
##     Leopard seal
##     New Zealand fur seal
##     Northern fur seal
##     Ribbon seal
##     Ringed seal
##     South African fur seal
##     South American fur seal
##     Southern elephant seal
## 
## MISSING in the lookup the following species in target='whale'.
##     Velvet whalefish
## 
## MISSING in the lookup the following species in target='sea lion'.
##     New Zealand sea lion
##     Seals and sea lions nei
##     South American sea lion
##     Steller sea lion
## 
## MISSING in the lookup the following species in target='dolphin'.
##     Common dolphinfish
# check for species in lookup not found in data
l_missing_d <-  setdiff(sp2grp$species, spgroups)
if (length(l_missing_d)>0){
  cat(sprintf('\nMISSING: These species in the lookup are not found in the FAO data \n'))
  print(l_missing_d)
}


## filter data to include only target species ----
m2 <-  m %>%
  dplyr::filter(species %in% sp2grp$species)

unique(m2$area) # confirm these are all marine
##  [1] Marine areas outside the Antarctic Antarctic areas nei               
##  [3] Atlantic, Western Central          Atlantic, Eastern Central         
##  [5] Atlantic, Southwest                Pacific, Northwest                
##  [7] Mediterranean and Black Sea        Pacific, Southeast                
##  [9] Pacific, Western Central           Atlantic, Northeast               
## [11] Atlantic, Northwest                Pacific, Eastern Central          
## [13] Indian Ocean, Eastern              Indian Ocean, Western             
## [15] Pacific, Southwest                 Pacific, Northeast                
## 29 Levels:  Africa - Inland waters ... Pacific, Western Central

7 Summarize data

# spread wide to expand years
m_w = m2 %>%
  tidyr::spread(year, value) %>%
  dplyr::left_join(sp2grp, by='species'); head(m_w)
## Warning: Column `species` joining character vector and factor, coercing
## into character vector
##     country               species                               area 1950
## 1 Argentina     Baleen whales nei Marine areas outside the Antarctic   NA
## 2 Argentina            Blue whale                Antarctic areas nei    7
## 3 Argentina    Bottlenose dolphin Marine areas outside the Antarctic   NA
## 4 Argentina Burmeister's porpoise Marine areas outside the Antarctic   NA
## 5 Argentina   Commerson's dolphin Marine areas outside the Antarctic   NA
## 6 Argentina        Common dolphin Marine areas outside the Antarctic   NA
##   1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964
## 1   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA
## 2   19    2    2    9    4    2    1    1    1    6    0    0    0    0
## 3   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA
## 4   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA
## 5   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA
## 6   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA
##   1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978
## 1   NA   NA   NA   NA   NA    0    0    0    0    0    0    0    0    0
## 2    0    0    0    0    0    0    0    0    0    0    0    0    0    0
## 3   NA   NA   NA   NA   NA    0    0    0    0    0    0    0    0    0
## 4   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA
## 5   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA
## 6   NA   NA   NA   NA   NA    0    0    0    0    0    0    0    0    0
##   1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992
## 1    0    0    0    0    0    0    0    0    0    0    0    0    0    0
## 2    0    0    0    0    0    0    0    0    0    0    0    0    0    0
## 3    0    0    0    0    0    0    0    0    0    0    0    0    0    0
## 4   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA
## 5   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA
## 6    0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## 1    0    0    0    0    0    0    0    0    0    0    1    0    0    0
## 2    0    0    0    0    0    0    0    0    0    0    0    0    0    0
## 3    0    0    0    0    0    0    0    0    0    0    0    0    1    0
## 4   NA   NA   NA   NA   NA    0    0    0    0    0    0    0    0    1
## 5   NA   NA   NA   NA   NA    0   12  212   37   40   16   12   24   14
## 6    0    0    0    0    0    0    0    0    0    0    0    0    0    0
##   2007 2008 2009 2010 2011 2012 2013 2014 2015 2016   target
## 1    0    0    0    0    0    0    0    0    0    0 cetacean
## 2    0    0    0    0    0    0    0    0    0    0 cetacean
## 3    0    0    0    0    0    0    0    0    0    0 cetacean
## 4    0    0    0    0    5    0    0    0    0    0 cetacean
## 5    0    0   21   21   11    0    0    0    0    0 cetacean
## 6    1    0    0    0    1    0    0    0    0    0 cetacean
# gather long by target
m_l = m_w %>%
  dplyr::select(-area) %>%
  tidyr::gather(year, value, -country, -species, -target, na.rm=T) %>%
  dplyr::mutate(year = as.integer(as.character(year))) %>%
  dplyr::arrange(country, target, year); head(m_l)
##     country        species   target year value
## 1 Argentina     Blue whale cetacean 1950     7
## 2 Argentina      Fin whale cetacean 1950   503
## 3 Argentina Humpback whale cetacean 1950    12
## 4 Argentina    Minke whale cetacean 1950     0
## 5 Argentina      Sei whale cetacean 1950   372
## 6 Argentina    Sperm whale cetacean 1950    52
#Temporary note: data for Gabn goes oly to 2009


# explore Japan[210]
m_l %>% 
  dplyr::group_by(country, target, year) %>%
  dplyr::summarize(value = sum(value)) %>% 
  dplyr::filter(country == 'Japan', target == 'cetacean', year >= 2000) 
## # A tibble: 17 x 4
## # Groups:   country, target [1]
##    country target    year value
##    <fct>   <fct>    <int> <dbl>
##  1 Japan   cetacean  2000 19396
##  2 Japan   cetacean  2001 19072
##  3 Japan   cetacean  2002 19268
##  4 Japan   cetacean  2003 17955
##  5 Japan   cetacean  2004 16736
##  6 Japan   cetacean  2005 17083
##  7 Japan   cetacean  2006 15374
##  8 Japan   cetacean  2007 14173
##  9 Japan   cetacean  2008 10161
## 10 Japan   cetacean  2009 12357
## 11 Japan   cetacean  2010  7543
## 12 Japan   cetacean  2011  3890
## 13 Japan   cetacean  2012  2653
## 14 Japan   cetacean  2013  3301
## 15 Japan   cetacean  2014  3572
## 16 Japan   cetacean  2015  3033
## 17 Japan   cetacean  2016  2959
# summarize totals per region per year
m_sum = m_l %>%
  dplyr::group_by(country, year) %>%
  dplyr::summarize(value = sum(value, na.rm=TRUE)) %>%
  dplyr::filter(value != 0) %>%
  dplyr::ungroup(); head(m_sum) 
## # A tibble: 6 x 3
##   country    year value
##   <fct>     <int> <dbl>
## 1 Argentina  1950   946
## 2 Argentina  1951   796
## 3 Argentina  1952   798
## 4 Argentina  1953   678
## 5 Argentina  1954  1083
## 6 Argentina  1955   947

8 Assign country names to OHI regions

m_sum <- m_sum %>%
  dplyr::mutate(country = as.character(country)) %>%
  dplyr::mutate(country = ifelse(stringr::str_detect(country, "C.*te d'Ivoire"), "Ivory Coast", country))


### Function to convert to OHI region ID
m_sum_rgn <- name_2_rgn(df_in = m_sum, 
                       fld_name='country', 
                       flds_unique=c('year'))
## 
## These data were removed for not having any match in the lookup tables:
## 
## Other nei 
##         1 
## 
## These data were removed for not being of the proper rgn_type (eez,ohi_region) or mismatching region names in the lookup tables:
## < table of extent 0 x 0 >
## 
## DUPLICATES found. Consider using collapse2rgn to collapse duplicates (function in progress).
## # A tibble: 2 x 1
##   country   
##   <chr>     
## 1 Guadeloupe
## 2 Martinique
# these are duplicates for the same region
dplyr::filter(m_sum_rgn, country %in% c("Guadeloupe", "Martinique"))
## # A tibble: 57 x 5
##    country     year value rgn_id rgn_name                 
##    <chr>      <int> <dbl>  <int> <chr>                    
##  1 Guadeloupe  1970   0.1    140 Guadeloupe and Martinique
##  2 Guadeloupe  1971   0.1    140 Guadeloupe and Martinique
##  3 Guadeloupe  1972   0.1    140 Guadeloupe and Martinique
##  4 Guadeloupe  1973   0.1    140 Guadeloupe and Martinique
##  5 Guadeloupe  1974   0.1    140 Guadeloupe and Martinique
##  6 Guadeloupe  1975   0.1    140 Guadeloupe and Martinique
##  7 Guadeloupe  1976   0.1    140 Guadeloupe and Martinique
##  8 Guadeloupe  1977  20      140 Guadeloupe and Martinique
##  9 Guadeloupe  1978  20      140 Guadeloupe and Martinique
## 10 Guadeloupe  1979  10      140 Guadeloupe and Martinique
## # ... with 47 more rows
# They will be summed:
m_sum_rgn <- m_sum_rgn %>%
  dplyr::group_by(rgn_id, rgn_name, year) %>%
  dplyr::summarize(value = sum(value)) %>%
  dplyr::ungroup()

9 Scale the data and save files

Data is rescaled by dividing by the 95th quantile of values across all regions from 2011 to 2014.

target_harvest <- m_sum_rgn %>%
  dplyr::mutate(quant_95 = quantile(value[year %in% 2011:2016], 0.95, na.rm = TRUE)) %>%
  dplyr::mutate(score = value / quant_95) %>% 
  dplyr::mutate(score = ifelse(score>1, 1, score)) %>%
  dplyr::select(rgn_id, year, pressure_score = score) %>%
  dplyr::arrange(rgn_id, year); head(target_harvest); summary(target_harvest)
## # A tibble: 6 x 3
##   rgn_id  year pressure_score
##    <int> <int>          <dbl>
## 1      7  1984      0.0000294
## 2      7  1985      0.0000294
## 3      7  1986      0.0000294
## 4      7  1987      0.0000294
## 5      7  1988      0.0000294
## 6      7  1989      0.0000294
##      rgn_id           year      pressure_score     
##  Min.   :  7.0   Min.   :1950   Min.   :0.0000294  
##  1st Qu.:104.0   1st Qu.:1970   1st Qu.:0.0017647  
##  Median :141.0   Median :1984   Median :0.0307353  
##  Mean   :136.3   Mean   :1984   Mean   :0.2068458  
##  3rd Qu.:180.0   3rd Qu.:1998   3rd Qu.:0.2540441  
##  Max.   :231.0   Max.   :2016   Max.   :1.0000000
#quant_95= 160761


  # any regions that did not have a catch should have score = 0 
rgns <-  rgn_master %>%
  dplyr::filter(rgn_typ == "eez") %>%
  dplyr::select(rgn_id = rgn_id_2013) %>%
  dplyr::filter(rgn_id < 255) %>%
  base::unique() %>%
  dplyr::arrange(rgn_id)


  
rgns <- expand.grid(rgn_id = rgns$rgn_id, year = min(target_harvest$year):max(target_harvest$year))
  
target_harvest <-  rgns %>%
  dplyr::left_join(target_harvest) %>%
  dplyr::mutate(pressure_score = ifelse(is.na(pressure_score), 0, pressure_score)) %>%
  dplyr::arrange(rgn_id); head(target_harvest); summary(target_harvest)
## Joining, by = c("rgn_id", "year")
##   rgn_id year pressure_score
## 1      1 1950              0
## 2      1 1951              0
## 3      1 1952              0
## 4      1 1953              0
## 5      1 1954              0
## 6      1 1955              0
##      rgn_id           year      pressure_score   
##  Min.   :  1.0   Min.   :1950   Min.   :0.00000  
##  1st Qu.: 59.0   1st Qu.:1966   1st Qu.:0.00000  
##  Median :117.0   Median :1983   Median :0.00000  
##  Mean   :118.1   Mean   :1983   Mean   :0.03003  
##  3rd Qu.:177.0   3rd Qu.:2000   3rd Qu.:0.00000  
##  Max.   :250.0   Max.   :2016   Max.   :1.00000
  write.csv(target_harvest, 'output/fao_targeted.csv', row.names = FALSE)
  
target_harvest_gf <- target_harvest %>%
  dplyr::mutate(gapfill = 0) %>%
  dplyr::select(rgn_id, year, gapfill)
  
  write.csv(target_harvest_gf, 'output/fao_targeted_gf.csv', row.names = FALSE)

10 Data check

The data from last year and this year should be the same unless there were changes to underlying FAO data or the master species list.

In this case, all of the regions looked very similar.

new <- read.csv("output/fao_targeted.csv") %>% 
  filter(year==2015)

old <- read.csv("../v2017/output/fao_targeted.csv") %>%
  #mutate(year== year-2) %>% 
  dplyr::filter(year == 2015) %>%
  dplyr::select(rgn_id, year, pressure_score_old=pressure_score) %>%
  dplyr::left_join(new, by=c("rgn_id", "year"))
old
##     rgn_id year pressure_score_old pressure_score
## 1        1 2015       0.0000000000   0.0000000000
## 2        2 2015       0.0000000000   0.0000000000
## 3        3 2015       0.0000000000   0.0000000000
## 4        4 2015       0.0000000000   0.0000000000
## 5        5 2015       0.0000000000   0.0000000000
## 6        6 2015       0.0000000000   0.0000000000
## 7        7 2015       0.0000000000   0.0000000000
## 8        8 2015       0.0000000000   0.0000000000
## 9        9 2015       0.0000000000   0.0000000000
## 10      10 2015       0.0000000000   0.0000000000
## 11      11 2015       0.0000000000   0.0000000000
## 12      12 2015       0.0000000000   0.0000000000
## 13      13 2015       0.0000000000   0.0000000000
## 14      14 2015       0.0000000000   0.0000000000
## 15      15 2015       0.0000000000   0.0000000000
## 16      16 2015       0.0171040676   0.0188235294
## 17      17 2015       0.0000000000   0.0000000000
## 18      18 2015       0.0000000000   0.0000000000
## 19      19 2015       0.0000000000   0.0000000000
## 20      20 2015       0.3610561762   0.3973529412
## 21      21 2015       0.0000000000   0.0000000000
## 22      24 2015       0.0000000000   0.0000000000
## 23      25 2015       0.0000000000   0.0000000000
## 24      26 2015       0.0000000000   0.0000000000
## 25      28 2015       0.0000000000   0.0000000000
## 26      29 2015       0.0000000000   0.0000000000
## 27      30 2015       0.0000000000   0.0000000000
## 28      31 2015       0.0000000000   0.0000000000
## 29      32 2015       0.0000000000   0.0000000000
## 30      33 2015       0.0000000000   0.0000000000
## 31      34 2015       0.0000000000   0.0000000000
## 32      35 2015       0.0000000000   0.0000000000
## 33      36 2015       0.0000000000   0.0000000000
## 34      37 2015       0.0000000000   0.0000000000
## 35      38 2015       0.0000000000   0.0000000000
## 36      39 2015       0.0000000000   0.0000000000
## 37      40 2015       0.0000000000   0.0000000000
## 38      41 2015       0.0000000000   0.0000000000
## 39      42 2015       0.0000000000   0.0000000000
## 40      43 2015       0.0000000000   0.0000000000
## 41      44 2015       0.0000000000   0.0000000000
## 42      45 2015       0.0000000000   0.0000000000
## 43      46 2015       0.0000000000   0.0000000000
## 44      47 2015       0.0000000000   0.0000000000
## 45      48 2015       0.0000000000   0.0000000000
## 46      49 2015       0.0000000000   0.0000000000
## 47      50 2015       0.0000000000   0.0000000000
## 48      51 2015       0.0000000000   0.0000000000
## 49      52 2015       0.0000000000   0.0000000000
## 50      53 2015       0.0000000000   0.0000000000
## 51      54 2015       0.0000000000   0.0000000000
## 52      55 2015       0.0000000000   0.0000000000
## 53      56 2015       0.0000000000   0.0000000000
## 54      57 2015       0.0000000000   0.0000000000
## 55      58 2015       0.0000000000   0.0000000000
## 56      59 2015       0.0000000000   0.0000000000
## 57      60 2015       0.0000000000   0.0000000000
## 58      61 2015       0.0000000000   0.0000000000
## 59      62 2015       0.0000000000   0.0000000000
## 60      63 2015       0.0000000000   0.0000000000
## 61      64 2015       0.0000000000   0.0000000000
## 62      65 2015       0.0000000000   0.0000000000
## 63      66 2015       0.0000000000   0.0000000000
## 64      67 2015       0.0000000000   0.0000000000
## 65      68 2015       0.0000000000   0.0000000000
## 66      69 2015       0.0000000000   0.0000000000
## 67      70 2015       0.0000000000   0.0000000000
## 68      71 2015       0.0000000000   0.0000000000
## 69      72 2015       0.0000000000   0.0000000000
## 70      73 2015       0.0416911647   0.3314705882
## 71      74 2015       0.0000000000   0.0000000000
## 72      75 2015       0.0000000000   0.0000000000
## 73      76 2015       0.0000000000   0.0000000000
## 74      77 2015       0.0000000000   0.0000000000
## 75      78 2015       0.0000000000   0.0000000000
## 76      79 2015       0.0000000000   0.0000000000
## 77      80 2015       0.0000000000   0.0000000000
## 78      81 2015       0.0000000000   0.0000000000
## 79      82 2015       0.0000000000   0.0000000000
## 80      84 2015       0.0000000000   0.0000000000
## 81      85 2015       0.0000000000   0.0000000000
## 82      86 2015       0.0000000000   0.0000000000
## 83      88 2015       0.0000000000   0.0000000000
## 84      89 2015       0.0000000000   0.0000000000
## 85      90 2015       0.0000000000   0.0000000000
## 86      91 2015       0.0000000000   0.0000000000
## 87      92 2015       0.0000000000   0.0000000000
## 88      93 2015       0.0000000000   0.0000000000
## 89      94 2015       0.0000000000   0.0000000000
## 90      95 2015       0.0000000000   0.0000000000
## 91      96 2015       0.0000000000   0.0000000000
## 92      97 2015       0.0000000000   0.0000000000
## 93      98 2015       0.0000000000   0.0000000000
## 94      99 2015       0.0000000000   0.0000000000
## 95     100 2015       0.0000000000   0.0000000000
## 96     101 2015       0.0000000000   0.0000000000
## 97     102 2015       0.0000000000   0.0000000000
## 98     103 2015       0.0000000000   0.0000000000
## 99     104 2015       0.0000000000   0.0000000000
## 100    105 2015       0.0000000000   0.0000000000
## 101    106 2015       0.0000000000   0.0000000000
## 102    107 2015       0.0000000000   0.0000000000
## 103    108 2015       0.0000000000   0.0000000000
## 104    110 2015       0.0000000000   0.0000000000
## 105    111 2015       0.0000000000   0.0000000000
## 106    112 2015       0.0000000000   0.0000000000
## 107    113 2015       0.0000000000   0.0000000000
## 108    114 2015       0.0000000000   0.0000000000
## 109    115 2015       0.0000000000   0.0000000000
## 110    116 2015       0.0000000000   0.0000000000
## 111    117 2015       0.0000000000   0.0000000000
## 112    118 2015       0.0000000000   0.0000000000
## 113    119 2015       0.0000000000   0.0000000000
## 114    120 2015       0.0000000000   0.0000000000
## 115    121 2015       0.0000000000   0.0000000000
## 116    122 2015       0.0000000000   0.0000000000
## 117    123 2015       0.0000000000   0.0000000000
## 118    124 2015       0.0000000000   0.0000000000
## 119    125 2015       0.0005345021   0.0005882353
## 120    126 2015       0.0000000000   0.0000000000
## 121    127 2015       0.0000000000   0.0000000000
## 122    129 2015       0.0000000000   0.0000000000
## 123    130 2015       0.0000000000   0.0000000000
## 124    131 2015       0.0000000000   0.0000000000
## 125    132 2015       0.0000000000   0.0000000000
## 126    133 2015       0.0000000000   0.0000000000
## 127    134 2015       0.0000000000   0.0000000000
## 128    135 2015       0.0000000000   0.0000000000
## 129    136 2015       0.0000000000   0.0000000000
## 130    137 2015       0.0000000000   0.0000000000
## 131    138 2015       0.0000000000   0.0000000000
## 132    139 2015       0.0000000000   0.0000000000
## 133    140 2015       0.0000000000   0.0000000000
## 134    141 2015       0.0000000000   0.0000000000
## 135    143 2015       0.0491741942   0.0541176471
## 136    144 2015       0.0000000000   0.0000000000
## 137    145 2015       0.7520444706   0.9232352941
## 138    146 2015       0.0000000000   0.0000000000
## 139    147 2015       0.0000000000   0.0000000000
## 140    148 2015       0.0000000000   0.0000000000
## 141    149 2015       0.0000000000   0.0000000000
## 142    150 2015       0.0000000000   0.0000000000
## 143    151 2015       0.0000000000   0.0000000000
## 144    152 2015       0.0000000000   0.0000000000
## 145    153 2015       0.0000000000   0.0000000000
## 146    154 2015       0.0000000000   0.0000000000
## 147    155 2015       0.0000000000   0.0000000000
## 148    156 2015       0.0000000000   0.0000000000
## 149    157 2015       0.0000000000   0.0000000000
## 150    158 2015       0.0000000000   0.0000000000
## 151    159 2015       0.0000000000   0.0000000000
## 152    161 2015       0.0000000000   0.0000000000
## 153    162 2015       0.0138970549   0.0152941176
## 154    163 2015       0.0000000000   0.1108823529
## 155    164 2015       0.0000000000   0.0000000000
## 156    166 2015       0.0000000000   0.0000000000
## 157    167 2015       0.0000000000   0.0000000000
## 158    168 2015       0.0000000000   0.0000000000
## 159    169 2015       0.0000000000   0.0000000000
## 160    171 2015       0.0000000000   0.0000000000
## 161    172 2015       0.0000000000   0.0000000000
## 162    173 2015       0.0000000000   0.0000000000
## 163    174 2015       0.0000000000   0.0000000000
## 164    175 2015       0.0000000000   0.0000000000
## 165    176 2015       0.0000000000   0.0000000000
## 166    177 2015       0.0002672511   0.0002941176
## 167    178 2015       0.0000000000   0.0000000000
## 168    179 2015       0.0002672511   0.0002941176
## 169    180 2015       0.0058795232   0.0064705882
## 170    181 2015       0.0000000000   0.0000000000
## 171    182 2015       0.0016035063   0.0017647059
## 172    183 2015       0.0000000000   0.0000000000
## 173    184 2015       0.0000000000   0.0000000000
## 174    185 2015       0.0000000000   0.0000000000
## 175    186 2015       0.0000000000   0.0000000000
## 176    187 2015       0.0008017532   0.0008823529
## 177    188 2015       0.0000000000   0.0000000000
## 178    189 2015       0.0000000000   0.0000000000
## 179    190 2015       0.0000000000   0.0000000000
## 180    191 2015       0.0000000000   0.0000000000
## 181    192 2015       0.0000000000   0.0000000000
## 182    193 2015       0.0000000000   0.0000000000
## 183    194 2015       0.0000000000   0.0000000000
## 184    195 2015       0.0000000000   0.0000000000
## 185    196 2015       0.0000000000   0.0000000000
## 186    197 2015       0.0000000000   0.0000000000
## 187    198 2015       0.0000000000   0.0000000000
## 188    199 2015       0.0000000000   0.0000000000
## 189    200 2015       0.0000000000   0.0000000000
## 190    202 2015       0.0000000000   0.0000000000
## 191    203 2015       0.0000000000   0.0000000000
## 192    204 2015       0.0000000000   0.0000000000
## 193    205 2015       0.0000000000   0.0000000000
## 194    206 2015       0.0000000000   0.0000000000
## 195    207 2015       0.0000000000   0.0000000000
## 196    208 2015       0.0000000000   0.0000000000
## 197    209 2015       0.0000000000   0.0000000000
## 198    210 2015       0.8284782725   0.8920588235
## 199    212 2015       0.0000000000   0.0000000000
## 200    213 2015       0.0000000000   0.0000000000
## 201    214 2015       0.0000000000   0.0000000000
## 202    215 2015       0.0000000000   0.0000000000
## 203    216 2015       0.0106900422   0.0526470588
## 204    218 2015       0.0000000000   0.0000000000
## 205    219 2015       0.0000000000   0.0000000000
## 206    220 2015       0.0000000000   0.0000000000
## 207    221 2015       0.0000000000   0.0000000000
## 208    222 2015       0.0000000000   0.0000000000
## 209    223 2015       0.1763856967   0.1941176471
## 210    224 2015       0.0000000000   0.0000000000
## 211    227 2015       0.0000000000   0.0000000000
## 212    228 2015       0.0000000000   0.0000000000
## 213    231 2015       0.0002672511   0.0002941176
## 214    232 2015       0.0000000000   0.0000000000
## 215    237 2015       0.0000000000   0.0000000000
## 216    244 2015       0.0000000000   0.0000000000
## 217    245 2015       0.0000000000   0.0000000000
## 218    247 2015       0.0000000000   0.0000000000
## 219    248 2015       0.0000000000   0.0000000000
## 220    249 2015       0.0000000000   0.0000000000
## 221    250 2015       0.0000000000   0.0000000000
plot(pressure_score ~ pressure_score_old, data=old)
abline(0, 1, col="red")

compare_plot <- ggplot(data = old, aes(x=pressure_score_old, y= pressure_score, label=rgn_id))+
  geom_point()+
  geom_abline(color="red")

plot(compare_plot)

ggplotly(compare_plot)